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# Senior Data Engineer

- **Company:** FLOATS, INC.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $174,893.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, BigQuery, Code Review, Databases, Data Infrastructure, Extract Transform Load (ETL), Software Debugging, Machine Learning, MongoDB, Software Engineering, SQL Databases, Type Systems, TypeScript, Large Language Models, Snowflake, AI Coding Agents, Event Driven Architecture, Debezium, Ripple (payment Protocol), Apache Kafka, Asynchronous Programming, Data Pipelines
- **Published:** October 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5d4b4eda0b8a99d9

## About the Role

* Applied ML/AI in production: You've shipped at least one ML or AI feature that customers use, and lived with it afterward. Recommendations, embeddings, search, LLM-powered features, or similar.
 * Pipeline ownership and debugging: You've been on the hook when a pipeline broke. You investigate with evidence (lineage, logs, data samples) rather than guesses, and you understand ELT/ETL, orchestration, backfills, and data quality from the inside out.
 * Feature delivery: Your data work has a visible output in the product. You've built pipelines and models that feed customer-facing features, not just internal tooling or reporting.
 * TypeScript/Node.js: You've written and maintained production-level TypeScript codebases and are comfortable with type systems and debugging.
 * SQL and databases: Complex analytical SQL, with an eye on warehouse cost and performance. MongoDB familiarity is a plus.
 * Software engineering practices: You design before coding, write tests, and do code reviews. You use AI coding tools productively while owning the quality of the output.
 * Documentation and written communication: You document as part of the work. Your write-ups stand on their own asynchronously.
 * Warehouse modelling: Familiar with dbt and a cloud warehouse (BigQuery or Snowflake). Deep expertise isn't required on day one.
 * Optimization and scheduling (Timefold, OR-Tools): Nice to have. Genuine interest in the problem space matters more than prior experience.

As a fully remote team, we're looking for someone comfortable with asynchronous communication as the default, which means you have previous remote experience and are comfortable using tools like Slack, Loom, and Linear to communicate as needed. Don't worry-you will have significant deep work time since we have very few meetings.

## Description

* Intelligence pipeline ownership: You'll take end-to-end ownership of the data pipelines that power NextGen and other Intelligence team features
 * Applied ML in production: You'll contribute to AI features in production, building the data infrastructure that makes those features work reliably.
 * Pipeline health and maintenance: Working alongside Davi and Roberto (the Data team), you'll ensure our pipelines and data infrastructure are reliable and well-documented.

Once you are a bit more settled, we expect that you will jump into the following projects:

 * Event-driven architecture: As Float's data needs evolve, you'll play a key role in how we introduce event streaming (Kafka/Debezium) into the stack, and will have the opportunity to contribute to architectural decisions, as well as implementing them.
 * Scheduling optimisation work: You'll develop familiarity with Timefold and Float's scheduling domain.
 * Cross-team data impact: Improvements to pipelines and infrastructure tend to ripple outward. Over time, you'll likely have opportunities to improve workflows beyond the Intelligence team.